[R] 'Fitting' a model at predefined points

From: akintayo holder <blakdogg_at_gmail.com>
Date: Thu 25 Jan 2007 - 21:53:37 GMT

 I have a linear model ("mod1 <- lm(V3~V1+V2) and I would like to get the model's prediction at values of V1 and V2 not included in the original sample.

samp <- read.table("data.dat",nrows=100) attach(samp)
out.poly <- lm(V3 ~ V1 + V2)

If I try to use out.poly to predict values for the function I run into problems. It seems that it isn't possible to use a new data frame for the predict() or fitted() functions.

predict(out.poly, data.frame(x=V1, y=V2)) uses the original data predict(out.poly, data.frame(x=V1, y=V2), newdata=gene.dat) - uses the original data also, complaining of the decrease in rows.

If any one could point me in the correct direction, it would be appreciated.


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